Doorbell camera package detection

ABSTRACT

A method for security and/or automation systems is described. In one embodiment, the method includes identifying image data from a signal, analyzing the image data based at least in part on a first parameter, identifying a presence of an object based at least in part on the analyzing, and detecting an object event based at least in part on the identifying.

CROSS REFERENCE

This application is a continuation of U.S. patent application Ser. No.14/575,830, entitled “DOORBELL CAMERA PACKAGE DETECTION,” filed Dec. 18,2014, the disclosure of which is incorporated herein by this referencein its entirety.

BACKGROUND

The present disclosure relates to security and/or automation systems,and more particularly to the security of packages delivered to or pickedup from a premises.

Security and automation systems are widely deployed to provide varioustypes of communication and functional features such as monitoring,communication, notification, and/or others. These systems may be capableof supporting communication with a user through a communicationconnection or a system management action.

With the advent of the Internet and online shopping has come an increasein the delivery of packages to homes, businesses, schools, etc., andwith this increase in package delivery has come an increased opportunityfor package theft and notifying users about information related topackages at a premises.

SUMMARY

The systems and methods described herein relate to camera packagedetection, and in some cases are related to external cameras (e.g.,doorbell cameras). A camera may be used to capture one or more images ofa premises. Image analysis may be performed on the one or more capturedimages to identify the presence of an object. The object may beidentified as a package based at least in part on the image analysis.Upon identifying the object as a package, the systems and methods maymonitor the package for unauthorized interaction.

In one embodiment, the method may include identifying image data from asignal, analyzing the image data based at least in part on a firstparameter, identifying a presence of an object based at least in part onthe analyzing, and detecting an object event based at least in part onthe identifying. The image data may include a first set of image dataand a second set of image data, the second set of image data beingcaptured after the first set of image data. The first parameter mayinclude image analysis data to detect at least one of shape, color,texture, material, and reflectivity of the image data. The image datamay include at least one of photo data and video data, motion detectiondata based at least in part on a motion of the object, and/or facialrecognition data.

In some embodiments, analyzing the image data may include comparing atleast a portion of an earlier set of image data with at least a portionof a later set of image data. Additionally, or alternatively, analyzingthe image data may include analyzing at least one subset of the imagedata. In some cases, the method may include sending a notification to auser based at least in part on the assessing. In some cases, anotification may be sent to a user based at least in part on whether aprobability of the object event exceeds a predetermined probabilitythreshold.

In some embodiments, analyzing the image data may be based at least inpart on a second parameter. Accordingly, the method may includeanalyzing a first parameter and analyzing a second parameter in relationto the analysis of the first parameter. In some cases, assessing theprobability of the object event may include assessing a firstprobability of the object event based at least in part on analyzing theimage data based at least in part on the first parameter, and assessinga second probability of the object event based at least in part onanalyzing the image data based at least in part on the second parameter.

A computing device configured for doorbell camera package detection isalso described. The computing device may include a processor and memoryin electronic communication with the processor. The memory may storecomputer executable instructions that when executed by the processorcause the processor to perform the steps of identifying image data froma signal, analyzing the image data based at least in part on a firstparameter, identifying a presence of an object based at least in part onthe analyzing, and detecting an object event based at least in part onthe identifying.

A non-transitory computer-readable storage medium storing computerexecutable instructions is also described. When the instructions areexecuted by a processor, the execution of the instructions may cause theprocessor to perform the steps of identifying image data from a signal,analyzing the image data based at least in part on a first parameter,identifying a presence of an object based at least in part on theanalyzing, and detecting an object event based at least in part on theidentifying.

The foregoing has outlined rather broadly the features and technicaladvantages of examples according to this disclosure so that thefollowing detailed description may be better understood. Additionalfeatures and advantages will be described below. The conception andspecific examples disclosed may be readily utilized as a basis formodifying or designing other structures for carrying out the samepurposes of the present disclosure. Such equivalent constructions do notdepart from the scope of the appended claims. Characteristics of theconcepts disclosed herein—including their organization and method ofoperation—together with associated advantages will be better understoodfrom the following description when considered in connection with theaccompanying figures. Each of the figures is provided for the purpose ofillustration and description only, and not as a definition of the limitsof the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of the presentdisclosure may be realized by reference to the following drawings. Inthe appended figures, similar components or features may have the samereference label. Further, various components of the same type may bedistinguished by following a first reference label with a dash and asecond label that may distinguish among the similar components. However,features discussed for various components—including those having a dashand a second reference label—apply to other similar components. If onlythe first reference label is used in the specification, the descriptionis applicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

FIG. 1 shows a block diagram relating to a security and/or an automationsystem, in accordance with various aspects of this disclosure;

FIG. 2 shows a block diagram of a device relating to a security and/oran automation system, in accordance with various aspects of thisdisclosure;

FIG. 3 shows a block diagram of a device relating to a security and/oran automation system, in accordance with various aspects of thisdisclosure;

FIG. 4 shows a block diagram relating to a security and/or an automationsystem, in accordance with various aspects of this disclosure;

FIG. 5 is a flow chart illustrating an example of a method relating to asecurity and/or an automation system, in accordance with various aspectsof this disclosure;

FIG. 6 is a flow chart illustrating an example of a method relating to asecurity and/or an automation system, in accordance with various aspectsof this disclosure; and

FIG. 7 is a flow chart illustrating an example of a method for securityand/or an automation system, in accordance with various aspects of thepresent disclosure.

DETAILED DESCRIPTION

The problems solved by the systems and methods described herein includerecognizing when certain packages are delivered and/or picked up andidentifying information related to the package. Current packagedelivery/pick-up services are not secure and related package deliverymethods include problems such as improper notifications, delayednotifications, theft, and/or failing to notify the user aboutinformation related to one or more delivered/picked-up packages (e.g.,delivery person, size of the package, timeliness, etc.).

The systems and methods solve these and other problems by using specificvisual parameters for detecting information related to packages. In someembodiments, the systems and methods may provide a notification based atleast in part on one or more probabilities that may be based at least inpart on information related to package delivery/pick-up.

Some embodiments relate to systems, methods, and related devices fordetecting information related to one or more packages that are deliveredand/or picked up, specifically related to security/home automationsystems.

The present systems and methods use computer vision technology (based ona camera) to detect information related to packages, includingdetecting: a package, a person carrying a package, characteristicsrelated to a person carrying a package (e.g., facial features, uniform,vehicle, etc.), environmental information (e.g., light, weather, etc.),and/or other information.

In some embodiments, the system may detect information about a packageincluding shape, size, color, labeling, material, etc. This may be basedat least in part on algorithms related to: image processing techniques,edge detection, segmentation, shape detection, deformable part models,figure detection, background subtraction, object detection, and/or otherinformation. In some embodiments, the detection may occur when a personis walking toward and/or away from an area (e.g., a front door) and/orwhen a package is left at an area.

The detection may be based on information that the system collects overtime. This information may include general parameters, parametersspecific to a structure, and/or may be based on one or more areasrelated areas. For example, the system may perform detection related toa nearby area in view—such as a front porch. The system may learn and/orstore data (image, video, etc.) specific to the porch area and thenperform a comparison of one piece of porch data (like a picture or avideo taken on Nov. 1, 2014 with another piece of porch data (like apicture or a video taken on Nov. 5, 2014) to identify the presence orabsence of a package. This learning may occur through image and/or videocapturing over a period of time and then the system may compare one ormore captured data points with one or more other captured data points.In some embodiments, after some data is captured, the later captureddata may include motion-triggered capturing events (i.e., when motionoccurs, the system then captures an image and/or a video and can thencompare it to the early data set). The system may compare learned data(stored in a device, in a panel, and/or through a wireless cloudnetwork, etc.) with the motion-triggered data.

The system may learn and/or store data relating to (as examples)lighting, texture, reflectivity, color, materials (stone, stucco,cardboard), shape, motion (street traffic, sidewalk traffic, etc.),behavioral patterns, and/or weather, etc. In some embodiments, thislearning and/or storing of data may be specific to a structure or ascenario. For example, the system may analyze certain data based onfacing a street (having constant movement via traffic), a wooded area(tree movement from wind), lighting conditions (with a western facinghome), etc.

In some embodiments, along with the comparison based on the data, thedetection may include examining movement related to a package. Forexample, by comparing porch data, the system may detect a package and itmay look at other data indicating motion near the porch or businessentrance (e.g., based on timing, proximity motion detection data, etc.)and use this data to aid in detection. The system may also analyze aperson's clothing, logo, nametags, clipboards, facial features, and/ormannerisms (gait, posture, etc.).

In some embodiments, using the other additional data may increase aprobability and/or a confidence level assessed. For example, identifyinga package's presence based on image and/or video data of an area mayyield a first probability and/or confidence level. Then, based onadditional comparisons, calculations, analysis, identifications, and/oractions, the probability and/or confidence level may increase, stay thesame, and/or decrease. For example, the probability may increase basedat least in part on motion detection data, person detection data,clothing detection data, shape detection data, and/or uniform detectiondata, etc.

Based on a package detection, a system may provide a notification to auser and/or to a deliverer. A notification that a package has beendelivered, picked up, and/or moved from a location (based at least inpart on a probability and/or confidence level) may be sent to a user viatext, email, social media, phone call, push notifications, and/orvoicemail, etc. In addition, a notification to a user and/or to adeliverer may be based on environmental (e.g., inclement weather) and/orother factors, which may dictate the type of notification and/orinfluence the probability threshold for a notification (i.e., lower thethreshold for any notification or a specific type of notification basedon inclement weather).

The system may also help with theft prevention by notifying a user abouta package and/or whether another person is approaching a door beforeand/or after a package has been delivered and/or picked up. In someembodiments, the system may capture detection data related to movementand/or other parameters to assist in determining probabilities relatedto preventing theft. In addition, in some cases, the system may alsosound a visual and/or an audible alarm in response to certain analysis,data, identification, probabilities, and/or object events at leastrelated to a potential theft. In addition, a notification to a deliveryperson may also be provided. For example, based on identifying that apackage is delivered, the system may tell the delivery person to leavethe package in a certain location (under a cover based on weather, in agarage, at a side door, with a receptionist, etc.) and/or provideadditional instruction.

The following description provides examples and is not limiting of thescope, applicability, and/or examples set forth in the claims. Changesmay be made in the function and/or arrangement of elements discussedwithout departing from the scope of the disclosure. Various examples mayomit, substitute, and/or add various procedures and/or components asappropriate. For instance, the methods described may be performed in anorder different from that described, and/or various steps may be added,omitted, modified, and/or combined. Also, features described withrespect to some examples may be added, omitted, modified, and/orcombined in other examples.

FIG. 1 illustrates an example of a communications system 100 inaccordance with various aspects of the disclosure. The communicationssystem 100 may include control panels 105, devices 115, and/or a network130. The network 130 may provide user authentication, encryption, accessauthorization, tracking, Internet Protocol (IP) connectivity, and otheraccess, calculation, modification, and/or functions. The control panels105 may interface with the network 130 through wired and/or wirelesscommunication links 132 and may perform communication configuration,adjustment, and/or scheduling for communication with the devices 115, ormay operate under the control of a controller. In various examples, thecontrol panels 105 may communicate—either directly or indirectly (e.g.,through network 130)—with each other over wired and/or wirelesscommunication links 134. Control panels 105 may communicate with a backend server—directly and/or indirectly—using one or more communicationlinks.

The control panels 105 may wirelessly communicate with the devices 115via one or more antennas. For example, the control panels 105 maycommunicate wirelessly with one or more cameras such as securitycameras, doorbell cameras, etc. Each of the control panels 105 mayprovide communication coverage for a respective geographic coverage area110. In some examples, control panels 105 may be referred to as acontrol device, a base transceiver station, a radio base station, anaccess point, a radio transceiver, or some other suitable terminology.The geographic coverage area 110 for a control panel 105 may be dividedinto sectors making up only a portion of the coverage area. Thecommunications system 100 may include control panels 105 of differenttypes. There may be overlapping geographic coverage areas 110 for one ormore different parameters, including different technologies, features,subscriber preferences, hardware, software, technology, and/or methods.For example, each control panel 105 may be related to one or morediscrete structures (e.g., a home, a business) and each of the one morediscrete structures may be related to one or more discrete areas. Inother examples, multiple control panels 105 may be related to the sameone or more discrete structures (e.g., multiple control panels relatingto a home and/or a business complex).

The devices 115 are dispersed throughout the communications system 100and each device 115 may be stationary and/or mobile. A device 115 mayinclude a security camera, a doorbell camera, a cellular phone, apersonal digital assistant (PDA), a wireless modem, a wirelesscommunication device, a handheld device, a tablet computer, a laptopcomputer, a cordless phone, a wireless local loop (WLL) station, adisplay device (e.g., TVs, computer monitors, etc.), a printer, asensor, and/or the like. A device 115 may also include or be referred toby those skilled in the art as a user device, a sensor, a smartphone, aBLUETOOTH® device, a Wi-Fi device, a mobile station, a subscriberstation, a mobile unit, a subscriber unit, a wireless unit, a remoteunit, a mobile device, a wireless device, a wireless communicationsdevice, a remote device, an access terminal, a mobile terminal, awireless terminal, a remote terminal, a handset, a user agent, a mobileclient, a client, and/or some other suitable terminology. A device 115may include and/or be one or more sensors that sense: proximity, motion,temperatures, humidity, sound level, smoke, structural features (e.g.,glass breaking, window position, door position), time, geo-location dataof a user and/or a device, distance, biometrics, weight, speed, height,size, preferences, light, darkness, weather, time, system performance,and/or other inputs that relate to a security and/or an automationsystem. A device 115 may be able to communicate through one or morewired and/or wireless connections with various components such ascontrol panels, base stations, and/or network equipment (e.g., servers,wireless communication points, etc.) and/or the like.

The communication links 125 shown in communications system 100 mayinclude uplink (UL) transmissions from a device 115 to a control panel105, and/or downlink (DL) transmissions, from a control panel 105 to adevice 115. The downlink transmissions may also be called forward linktransmissions while the uplink transmissions may also be called reverselink transmissions. Each communication link 125 may include one or morecarriers, where each carrier may be a signal made up of multiplesub-carriers (e.g., waveform signals of different frequencies) modulatedaccording to the various radio technologies. Each modulated signal maybe sent on a different sub-carrier and may carry control information(e.g., reference signals, control channels, etc.), overhead information,user data, etc. The communication links 125 may transmit bidirectionalcommunications and/or unidirectional communications. Communication links125 may include one or more connections, including but not limited to,345 MHz, Wi-Fi, BLUETOOTH®, BLUETOOTH® Low Energy, cellular, Z-WAVE®,802.11, peer-to-peer, LAN, WLAN, Ethernet, fire wire, fiber optic,and/or other connection types related to security and/or automationsystems.

In some embodiments, of communications system 100, control panels 105and/or devices 115 may include one or more antennas for employingantenna diversity schemes to improve communication quality andreliability between control panels 105 and devices 115. Additionally oralternatively, control panels 105 and/or devices 115 may employmultiple-input, multiple-output (MIMO) techniques that may takeadvantage of multi-path, mesh-type environments to transmit multiplespatial layers carrying the same or different coded data.

While the devices 115 may communicate with each other through thecontrol panel 105 using communication links 125, each device 115 mayalso communicate directly with one or more other devices via one or moredirect communication links 134. Two or more devices 115 may communicatevia a direct communication link 134 when both devices 115 are in thegeographic coverage area 110 or when one or neither devices 115 iswithin the geographic coverage area 110. Examples of directcommunication links 134 may include Wi-Fi Direct, BLUETOOTH®, wired,and/or, and other P2P group connections. The devices 115 in theseexamples may communicate according to the WLAN radio and basebandprotocol including physical and MAC layers from IEEE 802.11, and itsvarious versions including, but not limited to, 802.11b, 802.11g,802.11a, 802.11n, 802.11ac, 802.11ad, 802.11ah, etc. In otherimplementations, other peer-to-peer connections and/or ad hoc networksmay be implemented within communications system 100.

The communications system 100 may be configured to monitor an areaoutside a door of a home or business. In conjunction with the controlpanels 105, devices 115, network 130, and communication links 125 and/or134, the communication system 100 may be configured to detect a packageat the area outside the door of the home or business. For example, adoorbell camera (e.g., sensor 115), may be configured to capture onemore or more images of the area, and based on the captured one or moreimages, the communication system 100 may detect a package beingdelivered to the area, being taken from the area, left at the area, etc.In some cases, the control panels 105 and/or devices 115 may performimage analysis on the one or more captured images to identify thepresence of an object, and based on the image analysis, may identify theobject as a package. Upon identifying the object as a package, one ormore elements of the communication system 100 may be configured tomonitor the package to detect events related to the package such as aperson detected within view of the package, delivery of the package,pickup of the package, etc.

FIG. 2 shows a block diagram 200 of an apparatus 205 for use inelectronic communication, in accordance with various aspects of thisdisclosure. In one embodiment, the apparatus 205 may be an example ofone or more aspects of a control panel 105 described with reference toFIG. 1. In some embodiments, apparatus 205 may be an example of asecurity camera such as a doorbell camera, as illustrated by device 115.

The apparatus 205 may include a receiver module 210, a package detectionmodule 215, and/or a transmitter module 220. The apparatus 205 may alsobe or include a processor. Each of these modules may be in communicationwith each other—directly and/or indirectly.

The components of the apparatus 205 may, individually or collectively,be implemented using one or more application-specific integratedcircuits (ASICs) adapted to perform some or all of the applicablefunctions in hardware. Alternatively, the functions may be performed byone or more other processing units (or cores), on one or more integratedcircuits. In other examples, other types of integrated circuits may beused (e.g., Structured/Platform ASICs, Field Programmable Gate Arrays(FPGAs), and other Semi-Custom ICs), which may be programmed in anymanner known in the art. The functions of each module may also beimplemented—in whole or in part—with instructions embodied in memoryformatted to be executed by one or more general and/orapplication-specific processors.

The receiver module 210 may receive information such as packets, userdata, and/or control information associated with various informationchannels (e.g., control channels, data channels, etc.). The receivermodule 210 may be configured to receive data and/or control informationfrom another device such as a control panel, sensor, and/or a camera.Information may be passed on to the package detection module 215, and toother components of the apparatus 205.

Package detection module 215 enables a method for package detection inconjunction with a security and/or automation system. Upon receiving oneor more images and/or videos at the receiver module 210, packagedetection module 215 performs image analysis based at least in part onthe one or more images and/or videos to detect a package and/or otherfeature. Upon detecting a package and/or other feature, the packagedetection module 215 monitors the package and determines whether aperson that is delivering, moving, and/or removing the detected packageis authorized to do so. The apparatus 205 may perform various functionsbased on this analysis and/or determination.

The transmitter module 220 may transmit the one or more signals receivedfrom other components of the apparatus 205. The transmitter module 220may transmit data and/or controls signals to a control panel and/orsensor associated with the security and/or automation system. The dataand/or control signals transmitted by the transmitter module 220 may beassociated with the image/video analysis and package/feature detectionperformed by the package detection module 215. In some examples, thetransmitter module 220 may be co-located with the receiver module 210 ina transceiver module.

FIG. 3 shows a block diagram 300 of an apparatus 205-a for use inwireless communication, in accordance with various examples. Theapparatus 205-a may be an example of one or more aspects of a controlpanel 105 and/or a device 115 described with reference to FIG. 1. It mayalso be an example of an apparatus 205 described with reference to FIG.2. The apparatus 205-a may include a receiver module 210-a, a packagedetection module 215-a, and/or a transmitter module 220-a, which may beexamples of the corresponding modules of apparatus 205. The apparatus205-a may also include a processor. Each of these components may be incommunication with each other. The package detection module 215-a mayinclude imaging sub-module 305, analysis sub-module 310, objectidentification sub-module 315, and probability sub-module 320. Thereceiver module 210-a and the transmitter module 220-a may perform thefunctions of the receiver module 210 and the transmitter module 220, ofFIG. 2, respectively.

In one embodiment, imaging sub-module 305 may identify image data from asignal. The image data may include at least one of photo data and videodata. In some cases, the image data may include data captured within anelectromagnetic spectrum such as the visual spectrum, infrared spectrum,etc. For example, imaging sub-module 305 may capture real-time viewsfrom a camera sensor of a camera and capture image data from thecaptured images. For example, the imaging sub-module 305 may captureimages from a security camera such as a doorbell camera or other type ofcamera located at a home, an office, and/or other type of building.

In one embodiment, analysis sub-module 310 may analyze the image databased at least in part on a first parameter. The first parameter mayinclude image analysis data to detect at least one of shape, color,texture, material, and/or reflectivity of the image data, among otherthings. For example, analysis sub-module 310 may perform image analysison the image data to detect distinguishable features. In some cases,analysis sub-module 310 may examine one or more pixels of an image todetermine whether the one or more pixels includes a feature of interest.In some embodiments, analysis sub-module 310 may detect a face, head,torso, arms, and/or legs of a user in an image. In some embodiments,analysis sub-module 310 may detect features of the user's head and/orface. In some embodiments, analysis sub-module 310 may detect an edge,corner, interest point, blob, and/or ridge in a captured image. An edgemay be points of an image where there is a boundary (or an edge) betweentwo image regions, or a set of points in the image which have arelatively strong gradient magnitude. Corners and interest points may beused interchangeably. For example, analysis sub-module 310 may detect anedge and/or corner of a box or package. The box or package may bedetected on a surface or being carried by a person such as a deliveryperson. An interest point may refer to a point-like feature in an image,which has a local two dimensional structure. In some embodiments, theanalysis sub-module 310 may search for relatively high levels ofcurvature in an image gradient to detect an interest point and/or corner(e.g., corner of an eye, corner of a mouth). Thus, the analysissub-module 310 may detect in an image of a user's face the corners ofthe eyes, eye centers, pupils, eye brows, point of the nose, nostrils,corners of the mouth, lips, center of the mouth, chin, ears, forehead,cheeks, and the like. A blob may include a complementary description ofimage structures in terms of regions, as opposed to corners that may bepoint-like in comparison. Thus, in some embodiments, the analysissub-module 310 may detect a smooth, non-point-like area (i.e., blob) inan image.

Additionally, or alternatively, in some embodiments, the analysissub-module 310 may detect a ridge of points in the image. In someembodiments, the analysis sub-module 310 may extract a local image patcharound a detected feature in order to track the feature in other imagessuch as previously and/or subsequently captured images. Accordingly, thefirst parameter may include at least one of edge, corner, interestpoint, blob, ridge, shape, color, texture, material, and/or reflectivityof the image data. In some cases, the first parameter may include alogo, icon, and/or symbol. For example, in some embodiments, analysissub-module 310 may detect a logo of a shipping company. In some cases,analysis sub-module 310 may detect a shape such as a logo on a surfaceof a box or package. Additionally, or alternatively, analysis sub-module310 may detect a logo on a uniform.

In one example, analysis sub-module 310 may detect a UPS® logo on thesurface of a package left on a porch, a logo on a uniform of a UPS®delivery person, and/or a logo on a UPS® delivery truck. This detectionand related analysis may be performed based at least in part on acommunication referencing a remote source having a repository ofinformation such as shapes, logos, colors, tracking numbers, QR codes,bar codes, etc. In some cases, analysis sub-module 310 may analyze theimage data based at least in part on a second parameter. Thus, as oneexample, analysis sub-module 310 may analyze a detected featureassociated with a delivery person, uniform or truck as a first parameter(e.g., facial recognition, logo, etc.), and may analyze a detectedfeature associated with a package as a second parameter (e.g., edge,corner, color, shape, size, logo, etc.). In one embodiment, analysissub-module 310 may analyze at least one subset of the image data.

In some cases, object identification sub-module 315 may identify apresence of an object based at least in part on the analyzing a capturedimage to identify image data. For example, based on the detection of anedge, corner, shape, size, and/or logo in a captured image, for example,object identification sub-module 315 may identify the presence of acertain object such as a box or package, a delivery person, and/or adelivery truck. Accordingly, in some cases, the image data may includemotion detection data based at least in part on a motion of the detectedobject, and/or the image data may include facial recognition data. Insome cases, the image data may include a first set of image data and asecond set of image data. The second set of image data may be visualdata captured and/or detected after the first set of image data iscaptured and/or detected. Alternatively, the second set of image datamay be visual data captured and/or detected as a subset of a first setof image data.

In some embodiments, analysis sub-module 310 may compare at least aportion of an earlier set of image data with at least a portion of alater set of image data. For example, in conjunction with a packagedelivery, analysis sub-module 310 may compare an image of an area of apatio before a package is placed within the area and after the packageis placed within the area. Similar for a package pickup, analysissub-module 310 may compare an image of an area of a patio before thepackage, placed in the area of the patio, is retrieved and after thepackage is retrieved. In some cases, analysis sub-module 310 may queryan online database to verify a time of delivery and/or pickup. Forexample, package detection module 215 may be configured to expect apackage delivery and/or package pickup. Accordingly, analysis sub-module310 may query an online database associated with a delivery service suchUPS®, FEDEX®, DHL®, a user's email, etc. Thus, upon detecting a packagedelivery and/or pickup, analysis sub-module 310 may query the onlinedatabase to determine the status of the delivery or pickup. As oneexample, analysis sub-module 310 may query the database and determinethat the delivery time of a package is at 3:00 P.M. The analysissub-module 310 may then determine that the analysis of the image dataindicates that a package was detected within a predetermined range ofthe delivery time indicated by the online database (e.g., within 5minutes). Accordingly, analysis sub-module 310 may confirm that thedetected package is associated with the expected delivery.

In one embodiment, probability sub-module 320 may assess a probabilityof an object event based at least in part on the identifying a presenceof an object. In some cases, probability sub-module 320 may assess aprobability of an object event based on the identifying a presence of anobject satisfying a predetermined threshold. In some embodiments,probability sub-module 320 may assess a first probability of the objectevent based at least in part on analyzing the image data based at leastin part on a first parameter. Additionally, or alternatively,probability sub-module 320 may assess a second probability of the objectevent based at least in part on analyzing the image data based at leastin part on a second parameter. In one embodiment, probability sub-module320 may extract features such as lines, shapes, color segments, OCRtexts, deformed parts, etc., from one or more sampled frames (images)from one or more photos and/or video.

Probability sub-module 320 may make the extract features part of aBayesian network. Probability sub-module 320 may then compute, forexample, the probability of a person with a package present in an imageusing a Monte Carlo Markov Chain algorithm (MCMC). Additionally, oralternatively, probability sub-module 320 may use a Recursive BayesFilter to combine probabilities from each sample and compute a jointprobability, potentially increasing the accuracy of the probability. Insome cases, probability sub-module 320 may incorporate moving patternsin the computation of probability. For example, probability sub-module320 may detect movement of a package from one image to the next.

Probability sub-module 320 may be included in a computing device such asa desktop or laptop, an automation/security control panel, a remoteserver, and/or a sensor such as a doorbell camera. The probabilitysub-module 320 may compute a probability at a sensor, on a computingdevice, at a control panel, using a network connection, and/or on aremote server. The imaging sub-module 305 may capture one or more imagesof an object. The analysis sub-module 310 may perform image analysis onthe one or more captured images, and the object identificationsub-module 315 may identify the object in the one or more capturedimages. The probability sub-module 320 may then compute a probabilitythat the identified object is a package for delivery and/or pickup,and/or a person or other identifier associated with a delivery, pickup,and/or event. A processor may execute software code in conjunction withthe probability sub-module 320 to compute a probability that an objectin a captured image is a package. A storage medium and/or memory maystore the software code.

Factors that may affect the probability include the availability ofimage analysis data to detect shape, color, texture, material, and/orreflectivity of the image data, among others. In some cases, probabilitymay depend on the ability of the probability sub-module 320 to detect anedge, corner, interest point, blob, and/or ridge in a captured image. Insome embodiments, a user may provide feedback in relation to aprobability assessment made by the probability sub-module 320. The usermay indicate whether the probability was accurate or not.

For example, the user may verify that the object identified by theobject identification sub-module 315 and assessed to be a package by theprobability sub-module 320 was indeed a package, a person, etc. Theprobability sub-module 320 may use the user's feedback to adjust acalculation of a probability. Accordingly, the probability sub-module320 may learn whether its probability is accurate. In some cases, theprobability sub-module 320 may reassess or reanalyze information toupdate and/or modify a probability and/or assessment that an objectevent occurred. For example, the probability sub-module 320 may receiveinformation indicating a delivery confirmation. Accordingly, theprobability sub-module 320 may reassess the probability that theidentified object is a package, increasing or decreasing the calculatedprobability.

FIG. 4 shows a system 400 for use in security and/or automation systems,in accordance with various examples. System 400 may include an apparatus205-b, which may be an example of the control panels 105 of FIG. 1and/or another device. Apparatus 205-b may also be an example of one ormore aspects of apparatus 205 of FIGS. 2 and 3 and/or device 115 ofFIG. 1. Apparatus 205-b may include notification module 445. In someembodiments, the terms a control panel and a control device are usedsynonymously.

Apparatus 205-b may also include components for bi-directional voice anddata communications including components for transmitting communicationsand components for receiving communications. For example, apparatus205-b may communicate bi-directionally with one or more of device 115-a,one or more sensors 115-b, remote storage 140, and/or remote server 145.This bi-directional communication may be direct (e.g., apparatus 205-bcommunicating directly with remote storage 140) or indirect (e.g.,apparatus 205-b communicating indirectly with remote server 145 throughremote storage 140).

In one embodiment, notification module 445 may send a notification to auser based at least in part on probability sub-module 320 assessing aprobability of an object event occurring. For example, notificationmodule 445 may send one or more notifications to one or more users basedat least in part on whether the assessed probability exceeds apredetermined probability threshold. In some cases, the probabilitythreshold may be determined in relation to sensor calibration, sensorsensitivity, user preference, past probability calculations, probabilityfeedback loops, image quality, system limitations, etc. Upon determiningthe probability exceeds the predetermined probability threshold, thenotification module 445 may generate a notification. In some cases, thenotification may include a text message, an email, a computer generatedphone call and/or voicemail, and the like. In some cases, notificationmodule 445 may generate a notification in association with anautomation/security system. For example, notification module 445 maygenerate an audio notification such as a chime and/or acomputer-generated voice announcement that is played at a control paneland/or using one or more speakers of the automation/security system. Insome cases, notification module 445 may generate an alarm upon certainobject events being detected. For example, upon the package detectionmodule 215 detecting a package delivery, notification module 445 maygenerate an alarm (e.g., via a siren, light, etc.) when the deliveredpackage is detected as being moved or picked up. As one example, packagedetection module 215 may determine whether a person approaching adelivered package is known or unknown, authorized or unauthorized, etc.For instance, package detection module 215 may use facial recognition,voice recognition, pattern detection/learning, device identification(e.g., detecting an identifier associated with a device carried by auser), etc., to determine whether a person approaching a deliveredpackage is known or unknown. Upon determining the person is unknown anddetecting the unknown person taking the package, notification module 445may sound an alarm. Similarly, package detection module 215 may monitora package left outside a premises for pickup to determine whether thepackage is taken by an authorized delivery person or not. A person maybe determined to be authorized to interact with the package based ondetection of a uniform, delivery truck, company logo, badge oridentification, barcode, etc. In some cases, authorization may bedetermined based on facial recognition, passcode query, and the like.For example, a user approaching the package may be prompted to provide aspoken code or a bade bar code in order to authorize interaction withthe package. For example, package detection module 215 may be configuredto detect a specified delivery service for delivery or pickup, such asUPS®, FEDEX®, DHL®, etc. Accordingly, package detection module 215 maydetermine whether the person is wearing a uniform of the expecteddelivery service, whether the delivery truck is from the expecteddelivery service, etc. Upon determining a package left for pickup isbeing taken by an unauthorized person, notification module 445 may soundan alarm.

Apparatus 205-b may also include a processor module 405, and memory 410(including software (SW) 415), an input/output controller module 420, auser interface module 425, a transceiver module 430, and one or moreantennas 435 each of which may communicate—directly or indirectly—withone another (e.g., via one or more buses 440). The transceiver module430 may communicate bi-directionally—via the one or more antennas 435,wired links, and/or wireless links—with one or more networks or remotedevices as described above.

For example, the transceiver module 430 may communicate bi-directionallywith one or more of device 115-a, remote storage 140, and/or remoteserver 145. The transceiver module 430 may include a modem to modulatethe packets and provide the modulated packets to the one or moreantennas 435 for transmission, and to demodulate packets received fromthe one or more antenna 435. While a control panel or a control device(e.g., 205-b) may include a single antenna 435, the control panel or thecontrol device may also have multiple antennas 435 capable ofconcurrently transmitting or receiving multiple wired and/or wirelesstransmissions. In some embodiments, one element of apparatus 205-b(e.g., one or more antennas 435, transceiver module 430, etc.) mayprovide a direct connection to a remote server 145 via a direct networklink to the Internet via a POP (point of presence). In some embodiments,one element of apparatus 205-b (e.g., one or more antennas 435,transceiver module 430, etc.) may provide a connection using wirelesstechniques, including digital cellular telephone connection, CellularDigital Packet Data (CDPD) connection, digital satellite dataconnection, and/or another connection.

The signals associated with system 400 may include wirelesscommunication signals such as radio frequency, electromagnetics, localarea network (LAN), wide area network (WAN), virtual private network(VPN), wireless network (using 802.11, for example), 345 MHz, Z-WAVE®,cellular network (using 3G and/or LTE, for example), and/or othersignals. The one or more antennas 435 and/or transceiver module 430 mayinclude or be related to, but are not limited to, WWAN (GSM, CDMA, andWCDMA), WLAN (including BLUETOOTH® and Wi-Fi), WMAN (WiMAX), antennasfor mobile communications, antennas for Wireless Personal Area Network(WPAN) applications (including RFID and UWB). In some embodiments, eachantenna 435 may receive signals or information specific and/or exclusiveto itself. In other embodiments, each antenna 435 may receive signals orinformation not specific or exclusive to itself.

In some embodiments, one or more sensors 115-b (e.g., motion, proximitysensor, smoke, glass break, door, window, carbon monoxide, and/oranother sensor) may connect to some element of system 400 via a networkusing one or more wired and/or wireless connections. These one or moresensors 115-b may provide input related to the systems and methodsdescribed here, including the sub-modules discussed for packagedetection module 215-a

In some embodiments, the user interface module 425 may include an audiodevice, such as an external speaker system, an external display devicesuch as a display screen, and/or an input device (e.g., remote controldevice interfaced with the user interface module 425 directly and/orthrough I/O controller module 420).

One or more buses 440 may allow data communication between one or moreelements of apparatus 205-b (e.g., processor module 405, memory 410, I/Ocontroller module 420, user interface module 425, etc.).

The memory 410 may include random access memory (RAM), read only memory(ROM), flash RAM, and/or other types. The memory 410 may storecomputer-readable, computer-executable software/firmware code 415including instructions that, when executed, cause the processor module405 to perform various functions described in this disclosure (e.g.,capturing one or more images, analyzing the images to detect a package,and monitoring the detected package, etc.). Alternatively, thesoftware/firmware code 415 may not be directly executable by theprocessor module 405 but may cause a computer (e.g., when compiled andexecuted) to perform functions described herein. Alternatively, thecomputer-readable, computer-executable software/firmware code 415 maynot be directly executable by the processor module 405 but may beconfigured to cause a computer (e.g., when compiled and executed) toperform functions described herein. The processor module 405 may includean intelligent hardware device, e.g., a central processing unit (CPU), amicrocontroller, an application-specific integrated circuit (ASIC), etc.

In some embodiments, the processor module 405 may include, among otherthings, an intelligent hardware device (e.g., a central processing unit(CPU), a microcontroller, and/or an ASIC, etc.). The memory 410 cancontain, among other things, the Basic Input-Output system (BIOS) whichmay control basic hardware and/or software operation such as theinteraction with peripheral components or devices. For example, thenotification module 445 to implement the present systems and methods maybe stored within the system memory 410. Applications resident withsystem 400 are generally stored on and accessed via a non-transitorycomputer readable medium, such as a hard disk drive or other storagemedium. Additionally, applications can be in the form of electronicsignals modulated in accordance with the application and datacommunication technology when accessed via a network interface (e.g.,transceiver module 430, one or more antennas 435, etc.).

Many other devices and/or subsystems may be connected to one or may beincluded as one or more elements of system 400 (e.g., entertainmentsystem, computing device, remote cameras, wireless key fob, wall mounteduser interface device, cell radio module, battery, alarm siren, doorlock, lighting system, thermostat, home appliance monitor, utilityequipment monitor, and so on). In some embodiments, all of the elementsshown in FIG. 4 need not be present to practice the present systems andmethods. The devices and subsystems can be interconnected in differentways from that shown in FIG. 4. In some embodiments, an aspect of someoperation of a system, such as that shown in FIG. 4, may be readilyknown in the art and are not discussed in detail in this application.Code to implement the present disclosure can be stored in anon-transitory computer-readable medium such as one or more of systemmemory 410 or other memory. The operating system provided on I/Ocontroller module 420 may be iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®,OS/2®, UNIX®, LINUX®, or another known operating system.

The transceiver module 430 may include a modem configured to modulatethe packets and provide the modulated packets to the antennas 435 fortransmission and/or to demodulate packets received from the antennas435. While the apparatus 205-b may include a single antenna 435, theapparatus 205-b may have multiple antennas 435 capable of concurrentlytransmitting and/or receiving multiple wireless transmissions.

The apparatus 205-b may include a package detection module 215-b, whichmay perform the functions described above for the package detectionmodules 215 of apparatus 205 of FIGS. 2 and/or 3.

FIG. 5 is a flow chart illustrating an example of a method 500 forsecurity and/or an automation system, in accordance with various aspectsof the present disclosure. For clarity, the method 500 is describedbelow with reference to aspects of one or more of the elements andfeatures described with reference to FIGS. 1 and/or 2, and/or aspects ofone or more of the elements and features described with reference toFIGS. 3 and/or 4. In some examples, one or more control panels, backendservers, devices, and/or sensors may execute one or more sets of codesto control the functional elements of the control panels, backendservers, devices, and/or sensors to perform the functions describedbelow. Additionally or alternatively, the control panels, backendservers, devices, and/or sensors may perform one or more of thefunctions described below using special-purpose hardware. Theoperation(s) of the method 500 may be performed using the packagedetection module 215 described with reference to FIGS. 2-4.

At block 505, image data may be identified from a signal. The image datamay include at least one of photo data and video data, motion detectiondata based at least in part on a motion of the object, and/or facialrecognition data, among other things. At block 510, the image data maybe analyzed based at least in part on a first parameter. The firstparameter may include image analysis features (e.g., edge detection,corners, blobs, etc.) to detect at least one of shape, color, texture,material, reflectivity of the image data, etc. In some cases, theanalysis may include comparing at least a portion of an earlier set ofimage data with at least a portion of a later set of image data.

At block 515, a presence of an object may be identified based at leastin part on the analyzing. Presence of an object may be detected bydetection of an edge, corner, interest point, blob, ridge, shape, color,texture, material, and/or reflectivity relative to the object, amongother things. In some cases, the presence of the object may continue tobe monitored to determine whether the object remains at the spot whereit was initially detected. The continual monitoring may occur viacontinuous, intermittent, and/or interval detection of an edge, corner,interest point, blob, ridge, shape, color, texture, material, and/orreflectivity relative to the object. At block 520, detect an objectevent based at least in part on the identifying.

Thus, the method 500 may provide for doorbell camera package detectionrelating to automation/security systems. It should be noted that themethod 500 is just one implementation and that the operations of themethod 500 may be rearranged or otherwise modified such that otherimplementations are possible.

FIG. 6 is a flow chart illustrating an example of a method 600 forsecurity and/or an automation system, in accordance with various aspectsof the present disclosure. For clarity, the method 600 is describedbelow with reference to aspects of one or more of the elements andfeatures described with reference to FIGS. 1 and/or 2, and/or aspects ofone or more of the elements and features described with reference toFIGS. 3 and/or 4. In some examples, a control panel, backend server,device, and/or sensor may execute one or more sets of codes to controlthe functional elements of the control panel, backend server, device,and/or sensor to perform the functions described below. Additionally oralternatively, the control panel, backend server, device, and/or sensormay perform one or more of the functions described below usingspecial-purpose hardware. The operation(s) of block 605 may be performedusing the package detection module 215 described with reference to FIGS.2-4.

At block 605, one or more images of an object at a premises may becaptured via a camera. At block 610, image analysis may be performed onthe one or more captured images. In some cases, the analysis may includecomparing two or more images captured over a certain time, motiondetection, facial recognition, symbol recognition such as detection of alogo of a delivery service, detecting points of interest and trackingthem from image to image via cross-correlation, etc. At block 615, theobject may be identified as a package based on the image analysis. Thepackage may be associated with a package delivery or a package pickup.

At block 620, the package may be monitored for unauthorized interaction.In some cases, further image analysis may be performed to determinewhether a person detected as approaching a package scheduled for pickupis an authorized delivery person of the delivery service associated withthe scheduled package pickup. In some cases, the monitoring may includeidentifying a logo on a uniform, an identification badge, a deliverytruck, etc. In some cases, the delivery person may display a badge to acamera. For example, upon detecting a person approaching the package,the package detection module 215 may provide a computer generated orpre-recorded voice prompt that requests the delivery person to displayhis or her identification card to the camera situated near the package(e.g., a doorbell camera situated at the front door of the premises). Insome cases, the package detection module 215 may query an onlinedatabase to confirm the tracking status of a package (e.g., “delivered,”“delivery pending,” “out for delivery,” “pickup pending,” “picked up,”etc.). Upon confirming the package delivery and/or package pickup, thenotification module 445 may generate a notification. Likewise, upondetecting unauthorized interaction with the package (e.g., someonetaking the package), the notification module 445 may generate anotification and/or sound an alarm.

Thus, the method 600 may provide for doorbell camera package detectionrelating to automation/security systems. It should be noted that themethod 600 is just one implementation and that the operations of themethod 600 may be rearranged or otherwise modified such that otherimplementations are possible.

FIG. 7 is a flow chart illustrating an example of a method 700 forsecurity and/or an automation system, in accordance with various aspectsof the present disclosure. For clarity, the method 700 is describedbelow with reference to aspects of one or more of the elements andfeatures described with reference to FIGS. 1 and/or 2, and/or aspects ofone or more of the elements and features described with reference toFIGS. 3 and/or 4. In some examples, a control panel, backend server,device, and/or sensor may execute one or more sets of codes to controlthe functional elements of the control panel, backend server, device,and/or sensor to perform the functions described below. Additionally oralternatively, the control panel, backend server, device, and/or sensormay perform one or more of the functions described below usingspecial-purpose hardware. The operation(s) of block 705 may be performedusing the package detection module 215 described with reference to FIGS.2-4.

At block 705, two or more images and/or videos may be captured of anarea of a premises to be used as a first set and a second set of imagedata. The camera, such as a doorbell camera, may be configured tocaptured images and/or videos of one or more areas of the premises. Atleast a portion of a first set of image data may be compared with atleast a portion of a second set of image data. In some cases, the firstset of image data may have been captured before the second set of imagedata. For example, an earlier captured image may be compared to a latercaptured image to detect a change between the captured images. Thechange may indicate the presence of an object within the view of thedoorbell camera. At block 710, an object may be identified in an imageand/or video based at least in part on image analysis of one or moreparameters. Image analysis may be used to detect at least one of edge,corner, interest point, blob, ridge, shape, color, texture, material,and/or reflectivity in relation to the identified object, among otherthings. At block 715, a probability of an object event may be assessedbased at least in part on the image analysis based at least in part onthe one or more parameters. The object event may include delivery of apackage, pickup of a package, movement of a package, a person carrying apackage, etc. At block 720, a notification may be sent to a user basedat least in part on the assessing of the probability. In some cases, anotification may be sent based at least in part on whether theprobability exceeds a predetermined probability threshold.

Thus, the method 700 may provide for doorbell camera package detectionrelating to automation/security systems. It should be noted that themethod 700 is just one implementation and that the operations of themethod 700 may be rearranged or otherwise modified such that otherimplementations are possible.

In some examples, aspects from the methods 500, 600, and 700 may becombined and/or separated. It should be noted that the methods 500, 600,and 700 are just example implementations, and that the operations of themethods 500, 600, and 700 may be rearranged or otherwise modified suchthat other implementations are possible.

The detailed description set forth above in connection with the appendeddrawings describes examples and does not represent the only instancesthat may be implemented or that are within the scope of the claims. Theterms “example” and “exemplary,” when used in this description, mean“serving as an example, instance, or illustration,” and not “preferred”or “advantageous over other examples.” The detailed description includesspecific details for the purpose of providing an understanding of thedescribed techniques. These techniques, however, may be practicedwithout these specific details. In some instances, known structures andapparatuses are shown in block diagram form in order to avoid obscuringthe concepts of the described examples.

Information and signals may be represented using any of a variety ofdifferent technologies and techniques. For example, data, instructions,commands, information, signals, bits, symbols, and chips that may bereferenced throughout the above description may be represented byvoltages, currents, electromagnetic waves, magnetic fields or particles,optical fields or particles, or any combination thereof.

The various illustrative blocks and components described in connectionwith this disclosure may be implemented or performed with ageneral-purpose processor, a digital signal processor (DSP), an ASIC, anFPGA or other programmable logic device, discrete gate or transistorlogic, discrete hardware components, or any combination thereof designedto perform the functions described herein. A general-purpose processormay be a microprocessor, but in the alternative, the processor may beany conventional processor, controller, microcontroller, and/or statemachine. A processor may also be implemented as a combination ofcomputing devices, e.g., a combination of a DSP and a microprocessor,multiple microprocessors, one or more microprocessors in conjunctionwith a DSP core, and/or any other such configuration.

The functions described herein may be implemented in hardware, softwareexecuted by a processor, firmware, or any combination thereof. Ifimplemented in software executed by a processor, the functions may bestored on or transmitted over as one or more instructions or code on acomputer-readable medium. Other examples and implementations are withinthe scope and spirit of the disclosure and appended claims. For example,due to the nature of software, functions described above can beimplemented using software executed by a processor, hardware, firmware,hardwiring, or combinations of any of these. Features implementingfunctions may also be physically located at various positions, includingbeing distributed such that portions of functions are implemented atdifferent physical locations.

As used herein, including in the claims, the term “and/or,” when used ina list of two or more items, means that any one of the listed items canbe employed by itself, or any combination of two or more of the listeditems can be employed. For example, if a composition is described ascontaining components A, B, and/or C, the composition can contain Aalone; B alone; C alone; A and B in combination; A and C in combination;B and C in combination; or A, B, and C in combination. Also, as usedherein, including in the claims, “or” as used in a list of items (forexample, a list of items prefaced by a phrase such as “at least one of”or “one or more of”) indicates a disjunctive list such that, forexample, a list of “at least one of A, B, or C” means A or B or C or ABor AC or BC or ABC (i.e., A and B and C).

In addition, any disclosure of components contained within othercomponents or separate from other components should be consideredexemplary because multiple other architectures may potentially beimplemented to achieve the same functionality, including incorporatingall, most, and/or some elements as part of one or more unitarystructures and/or separate structures.

Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage medium may be anyavailable medium that can be accessed by a general purpose or specialpurpose computer. By way of example, and not limitation,computer-readable media can comprise RAM, ROM, EEPROM, flash memory,CD-ROM, DVD, or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code means in the form of instructions ordata structures and that can be accessed by a general-purpose orspecial-purpose computer, or a general-purpose or special-purposeprocessor. Also, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, include compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk and Blu-ray discwhere disks usually reproduce data magnetically, while discs reproducedata optically with lasers. Combinations of the above are also includedwithin the scope of computer-readable media.

The previous description of the disclosure is provided to enable aperson skilled in the art to make or use the disclosure. Variousmodifications to the disclosure will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other variations without departing from the scope of thedisclosure. Thus, the disclosure is not to be limited to the examplesand designs described herein but is to be accorded the broadest scopeconsistent with the principles and novel features disclosed.

This disclosure may specifically apply to security system applications.This disclosure may specifically apply to automation systemapplications. In some embodiments, the concepts, the technicaldescriptions, the features, the methods, the ideas, and/or thedescriptions may specifically apply to security and/or automation systemapplications. Distinct advantages of such systems for these specificapplications are apparent from this disclosure.

The process parameters, actions, and steps described and/or illustratedin this disclosure are given by way of example only and can be varied asdesired. For example, while the steps illustrated and/or described maybe shown or discussed in a particular order, these steps do notnecessarily need to be performed in the order illustrated or discussed.The various exemplary methods described and/or illustrated here may alsoomit one or more of the steps described or illustrated here or includeadditional steps in addition to those disclosed.

Furthermore, while various embodiments have been described and/orillustrated here in the context of fully functional computing systems,one or more of these exemplary embodiments may be distributed as aprogram product in a variety of forms, regardless of the particular typeof computer-readable media used to actually carry out the distribution.The embodiments disclosed herein may also be implemented using softwaremodules that perform certain tasks. These software modules may includescript, batch, or other executable files that may be stored on acomputer-readable storage medium or in a computing system. In someembodiments, these software modules may permit and/or instruct acomputing system to perform one or more of the exemplary embodimentsdisclosed here.

This description, for purposes of explanation, has been described withreference to specific embodiments. The illustrative discussions above,however, are not intended to be exhaustive or limit the present systemsand methods to the precise forms discussed. Many modifications andvariations are possible in view of the above teachings. The embodimentswere chosen and described in order to explain the principles of thepresent systems and methods and their practical applications, to enableothers skilled in the art to utilize the present systems, apparatus, andmethods and various embodiments with various modifications as may besuited to the particular use contemplated.

1-20. (canceled)
 21. A method for a security and automation system of apremises, comprising: monitoring, by an image sensor of the security andautomation system, an area of the premises; analyzing one or more imagescaptured by the image sensor based at least in part on the monitoring;detecting an object within the area of the premises based at least inpart on the analyzing of the one or more images; analyzing one or morecharacteristics of the object based at least in part on the detecting ofthe object; computing a probability that the detected object is apackage based at least in part on the analyzing of the one or morecharacteristics of the object; and determining the object is a packagebased at least in part on the computing of the probability.
 22. Themethod of claim 21, further comprising: determining the probabilitysatisfies a predetermined threshold; and sending a notification to adevice associated with a user based at least in part on the probabilitysatisfying the threshold.
 23. The method of claim 22, furthercomprising: configuring the threshold based at least in part on acalibration of the image sensor, or a sensitivity of the image sensor,or a user profile, or a prior probability calculation, or a probabilityfeedback loop, or a quality of the one or more images, or anycombination thereof.
 24. The method of claim 22, further comprising:detecting a person approaching the object based at least in part on themonitoring; analyzing images of the person approaching the objectcaptured by the image sensor based at least in part on the detecting ofthe person approaching the object; and identifying one or morecharacteristics of the person approaching the object based at least inpart on the analyzing.
 25. The method of claim 24, wherein the one ormore characteristics of the person include at least one of a detecteduniform color, or a detected uniform pattern, or a detected companylogo, or a detected nametag, or a detected delivery truck, or a detectedidentification badge, or a detected barcode, or a detected facialfeature, or a detected walking pattern, or a spoken passcode, or anycombination thereof.
 26. The method of claim 24, further comprising:adjusting the probability based at least in part on the one or morecharacteristics of the person.
 27. The method of claim 24, furthercomprising: querying an online database based at least in part on theprobability the object is a package; and identifying package informationassociated with the premises based at least in part on the querying. 28.The method of claim 27, further comprising: adjusting the probabilitybased at least in part on the package information associated with thepremises.
 29. The method of claim 27, wherein the package informationincludes at least one of a delivery time of a scheduled packagedelivery, or a pickup time of a scheduled package pickup, or a status ofthe scheduled package delivery, or a status of the scheduled packagepickup, or a combination thereof.
 30. The method of claim 27, furthercomprising: determining the object is associated with a scheduledpackage pickup based at least in part on the probability and theidentified package information; determining the object is being taken byan unauthorized person based at least in part on the one or morecharacteristics of the person approaching the object; and sounding analarm at the premises based at least in part on determining the objectis being taken by the unauthorized person.
 31. The method of claim 22,further comprising: identifying feedback from the user associated withthe notification; and adjusting the probability based at least in parton the identified feedback.
 32. The method of claim 22, furthercomprising: detecting an environmental condition associated with thepremises; and increasing or decreasing the threshold based at least inpart on an environmental condition associated with the premises.
 33. Themethod of claim 21, wherein the probability is computed by at least oneof a processor in a device enclosing the image sensor, or by a processorof a computing device at the premises, or by a processor of a controlpanel at the premises, or by a processor of a remote server that remotefrom the premises, or any combination thereof.
 34. An apparatus for asecurity and automation system of a premises, comprising: a processor;memory in electronic communication with the processor; and instructionsstored in the memory, the instructions being executable by the processorto: monitor, by an image sensor of the security and automation system,an area of the premises; analyze one or more images captured by theimage sensor based at least in part on the monitoring; detect an objectwithin the area of the premises based at least in part on the analyzingof the one or more images; analyze one or more characteristics of theobject based at least in part on the detecting of the object; compute aprobability that the detected object is a package based at least in parton the analyzing of the one or more characteristics of the object; anddetermine the object is a package based at least in part on thecomputing of the probability.
 35. The apparatus of claim 34, theinstructions being executable by the processor to: determine theprobability satisfies a predetermined threshold; and send a notificationto a device associated with a user based at least in part on theprobability satisfying the threshold.
 36. The apparatus of claim 35, theinstructions being executable by the processor to: configure thethreshold based at least in part on a calibration of the image sensor,or a sensitivity of the image sensor, or a user profile, or a priorprobability calculation, or a probability feedback loop, or a quality ofthe one or more images, or any combination thereof.
 37. The apparatus ofclaim 35, the instructions being executable by the processor to: detecta person approaching the object based at least in part on themonitoring; analyze images of the person approaching the object capturedby the image sensor based at least in part on the detecting of theperson approaching the object; and identify one or more characteristicsof the person approaching the object based at least in part on theanalyzing.
 38. The apparatus of claim 37, wherein the one or morecharacteristics of the person include at least one of a detected uniformcolor, or a detected uniform pattern, or a detected company logo, or adetected nametag, or a detected delivery truck, or a detectedidentification badge, or a detected barcode, or a detected facialfeature, or a detected walking pattern, or a spoken passcode, or anycombination thereof.
 39. A non-transitory computer-readable mediumstoring computer-executable code for a security and automation system ofa premises, the code executable by a processor to: monitor, by an imagesensor of the security and automation system, an area of the premises;analyze one or more images captured by the image sensor based at leastin part on the monitoring; detect an object within the area of thepremises based at least in part on the analyzing of the one or moreimages; analyze one or more characteristics of the object based at leastin part on the detecting of the object; compute a probability that thedetected object is a package based at least in part on the analyzing ofthe one or more characteristics of the object; and determine the objectis a package based at least in part on the computing of the probability.40. The non-transitory computer-readable medium of claim 39, the codeexecutable by the processor to: determine the probability satisfies apredetermined threshold; and send a notification to a device associatedwith a user based at least in part on the probability satisfying thethreshold.